Imagen de Portada

Can Adjusting Hyperparameters Lead to Green Deep Learning: An Empirical Study on Correlations between Hyperparameters and Energy Consumption of Deep Learning Models

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Wang, Taoran, Li, Yanhui, Ma, Mingliang, Chen, Lin, Zhou, Yuming
Formato:	Preprint
Publicado:	2026
Materias:	Software Engineering
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

Hyperparameter Optimization for AST Differencing
por: Martinez, Matias, et al.
Publicado: (2020)

Optimizing Large Language Model Hyperparameters for Code Generation
por: Arora, Chetan, et al.
Publicado: (2024)

An Empirical Study of the Realism of Mutants in Deep Learning
por: Ahmed, Zaheed, et al.
Publicado: (2025)

Investigating the Role of LLMs Hyperparameter Tuning and Prompt Engineering to Support Domain Modeling
por: Bulhakov, Vladyslav, et al.
Publicado: (2025)

Search-based Hyperparameter Tuning for Python Unit Test Generation
por: Lukasczyk, Stephan, et al.
Publicado: (2025)

Contrasting the Hyperparameter Tuning Impact Across Software Defect Prediction Scenarios
por: Rakha, Mohamed Sami, et al.
Publicado: (2025)

Green AI: A Preliminary Empirical Study on Energy Consumption in DL Models Across Different Runtime Infrastructures
por: Alizadeh, Negar, et al.
Publicado: (2024)

Common Challenges of Deep Reinforcement Learning Applications Development: An Empirical Study
por: Morovati, Mohammad Mehdi, et al.
Publicado: (2023)

Understanding LLM-Centric Challenges for Deep Learning Frameworks: An Empirical Analysis
por: Mu, Yanzhou, et al.
Publicado: (2025)

Pimp My LLM: Leveraging Variability Modeling to Tune Inference Hyperparameters
por: Zine, Nada, et al.
Publicado: (2026)

A Comprehensive Study of Bugs in Modern Distributed Deep Learning Systems
por: Ma, Xiaoxue, et al.
Publicado: (2025)

Towards Enhancing the Reproducibility of Deep Learning Bugs: An Empirical Study
por: Shah, Mehil B., et al.
Publicado: (2024)

An Empirical Analysis of Mobile Energy Consumption Across User Configurations
por: Oliveira, Wellington
Publicado: (2026)

An Empirical Study on Noisy Label Learning for Program Understanding
por: Wang, Wenhan, et al.
Publicado: (2023)

Beyond Accuracy: An Empirical Study on Unit Testing in Open-source Deep Learning Projects
por: Wang, Han, et al.
Publicado: (2024)

Energy Consumption of Dataframe Libraries for End-to-End Deep Learning Pipelines:A Comparative Analysis
por: Kumar, Punit, et al.
Publicado: (2025)

An Empirical Study of Fault Localisation Techniques for Deep Learning
por: Humbatova, Nargiz, et al.
Publicado: (2024)

Deep Learning-based Software Engineering: Progress, Challenges, and Opportunities
por: Chen, Xiangping, et al.
Publicado: (2024)

Feature-Aware Test Generation for Deep Learning Models
por: Chen, Xingcheng, et al.
Publicado: (2026)

An Exploratory Study on Automatic Identification of Assumptions in the Development of Deep Learning Frameworks
por: Yang, Chen, et al.
Publicado: (2024)

Context-Aware Fuzzing for Robustness Enhancement of Deep Learning Models
por: Wang, Haipeng, et al.
Publicado: (2024)

An Empirical Investigation of Pre-Trained Deep Learning Model Reuse in the Scientific Process
por: Synovic, Nicholas M., et al.
Publicado: (2026)

Energy Flow Graph: Modeling Software Energy Consumption
por: Rajput, Saurabhsingh, et al.
Publicado: (2026)

WITNESS: A lightweight and practical approach to fine-grained predictive mutation testing
por: Lu, Zeyu, et al.
Publicado: (2025)

Toward Understanding Deep Learning Framework Bugs
por: Chen, Junjie, et al.
Publicado: (2022)

SwarmUpdate: Hierarchical Software Updates and Deep Learning Model Patching for Heterogeneous UAV Swarms
por: Geng, Lin, et al.
Publicado: (2025)

Contexts Matter: An Empirical Study on Contextual Influence in Fairness Testing for Deep Learning Systems
por: Du, Chengwen, et al.
Publicado: (2024)

Mock Deep Testing: Toward Separate Development of Data and Models for Deep Learning
por: Manke, Ruchira, et al.
Publicado: (2025)

Optimization-Aware Test Generation for Deep Learning Compilers
por: Shen, Qingchao, et al.
Publicado: (2025)

A Comprehensive Study of Deep Learning Model Fixing Approaches
por: You, Hanmo, et al.
Publicado: (2025)

Subgraph-Oriented Testing for Deep Learning Libraries
por: Xie, Xiaoyuan, et al.
Publicado: (2024)

CDS4RAG: Cyclic Dual-Sequential Hyperparameter Optimization for RAG
por: Chen, Pengzhou, et al.
Publicado: (2026)

Improving Deep Learning Framework Testing with Model-Level Metamorphic Testing
por: Mu, Yanzhou, et al.
Publicado: (2025)

Learn to Code Sustainably: An Empirical Study on LLM-based Green Code Generation
por: Vartziotis, Tina, et al.
Publicado: (2024)

Deep Learning Based Code Generation Methods: Literature Review
por: Yang, Zezhou, et al.
Publicado: (2023)

DaiFu: In-Situ Crash Recovery for Deep Learning Systems
por: He, Zilong, et al.
Publicado: (2025)

Adaptable Teastore with Energy Consumption Awareness: A Case Study
por: De Medeiros, Henrique, et al.
Publicado: (2025)

Framework for On the Fly Input Refinement for Deep Learning Models
por: Rathnasuriya, Ravishka
Publicado: (2025)

The Impact of Hyperparameters on Large Language Model Inference Performance: An Evaluation of vLLM and HuggingFace Pipelines
por: Martinez, Matias
Publicado: (2024)

Deep Learning Framework Testing via Model Mutation: How Far Are We?
por: Mu, Yanzhou, et al.
Publicado: (2025)